• 文献标题:   Artificial intelligence techniques for modeling and optimization of the HDS process over a new graphene based catalyst
  • 文献类型:   Article
  • 作  者:   HAJJAR Z, KAZEMEINI M, RASHIDI A, TAYYEBI S
  • 作者关键词:   graphene, hydrodesulfurization, nanocatalysi, genetic algorithm, neural network
  • 出版物名称:   PHOSPHORUS SULFUR SILICON THE RELATED ELEMENTS
  • ISSN:   1042-6507 EI 1563-5325
  • 通讯作者地址:   Sharif Univ Technol
  • 被引频次:   6
  • DOI:   10.1080/10426507.2016.1166428
  • 出版年:   2016

▎ 摘  要

A Co-Mo/graphene oxide (GO) catalyst has been synthesized for the first time for application in a defined hydrodesulfurization (HDS) process to produce sulfur free naphtha. An intelligent model based upon the neural network technique has then been developed to estimate the total sulfur output of this process. Process operating variables include temperature, pressure, LHSV and H-2/feed volume ratio. The three-layer, feed-forward neural network developed consists of five neurons in a hidden layer, trained with Levenberg-Marquardt, back-propagation gradient algorithm. The predicted amount of residual total sulfur is in very good agreement with the corresponding experimental values revealing a correlation coefficient of greater than 0.99. In addition, a genetic algorithm (GA) has been employed to optimize values of total sulfur as well as reaction conditions.